Question Annotation After detecting entities and rewriting the question, the question is processed through a sequence of annotators, such as numerical annotator, date annotator, comparator annotator and predicate argument structure (PAS) annotator.

comparator annotators detect particular types of values and operators for those values that appear in the question.


The experiment consists of reproducing a number of trials presented in the Stars2 data collection task (which was originally carried out as a limited form of dialogue between speaker-hearer participant pairs) and by replacing the hearer participant for a simple web interface with no feedback.

we intend to reproduce a number of Stars2 dialogues in monologue situations not unlike those implemented in a number of prominent data collection tasks for REG, including TUNA (Gatt et al., 2007), GRE3D3 (Dale and Viethen, 2009) and others.


The issue of human variation in definite description lexicalisation has been addressed in a few REG studies.

the work in (Hervás et al., 2013) has extensively analysed the lexicalisation of referring expressions from the TUNA corpus (Gatt et al., 2007), and it has provided a number of insights on how human speakers may be grouped together according to their lexical preferences.


Given the close relation between personality and natural language, it is not surprising that the use of the Big Five model has been ubiquitous both in natural language understanding (Golbeck et al., 2011;Farnadi et al., 2013;Plank and Hovy, 2015;Najib et al., 2015) and NLG research (Mairesse and Walker, 2010;Marshall et al., 2015;Lukin et al., 2015).

the work in (Mairesse and Walker, 2011) has addressed a wide range of generation decisions that may be driven by a target personality profile.


In addition to the fundamental goal of producing unambiguous descriptions, different REG algorithms may also consider additional requirements.

a great deal of attention has been devoted to the question of humanlikeness or plausibility of the generated output (Gatt et al., 2009).


Regarding description length, we assume that the effects of time-constrained communication may be comparable to those observed in critical situations discussed in (Arts et al., 2011).

we expect that referring expressions produced under time-constrained conditions to be on average longer (and possibly overspecified) if compared to those produced under normal conditions.


In order to address the problem of grammatical voice, more fine-grained control over the communicative/information structure is needed.

one must be able to control what the theme/topic of the sentence is.


We used two kinds of data: MIDI 1 music files of classical piano compositions, and TV series subtitles for dialogues.

we used compositions from: Bach 2 , Beethoven, Chopin, Grieg, Schubert, and Schumann 3 .


Apart from the aesthetic aspect of the drawing itself, the feature we preferredsince it was in line with our objectiveswas the ability to easily interact with the graphs created by means of popups, clicks and drag features.

this package supports dynamic changes in the shapes and colors of nodes and edges under certain conditions as users explore the network.


We propose a novel annotation approach to address this dilemma.

we propose to build a dependency tree structure for the entire document where the nodes of the tree are events and time expressions, as well as a few pre-defined "meta" nodes that are not anchored to a span of text in the document The building blocks of this dependency structure are pairs of events and time expressions in which the child event/time expression depends on its parent event/time expression for its temporal interpretation.


Based on these considerations, we propose a novel definition of the reference time for time expressions: Definition 1 Time expression A is the reference time for time expression B, if B depends on A for its temporal location interpretation.

a time expression can depend solely on its reference time to be interpreted and normalized.


Overall, our approach confirms the hypothesis that world knowledge can help an AMR parser.

focusing on the types of errors identified in Section 3, the darker shaded bars in Figure 2 show the errors obtained in the presence of world knowledge.


Our experiments confirmed the hypothesis that some forms of world knowledge can improve existing AMR parsers.

we found that the combination of named entities and WordNet features outperforms other methods on almost all metrics, except for slightly lower SRL and negation scores.


In the historical sciences, research conducting and analyzing interviews with contemporary witnesses has become known as Oral History.

in Germany this research was focused above all on the period of National Socialism and the Second World War.


This feature provides the LSTM family with additional information in predictions, compared to regular Recurrent Neural Networks (RNNs) and Bag-of-Words (BOW) models.

lSTM networks assume the data to be chain-structured.


After defining the characteristics of the resources and testing an initial setup of an LSVR system, we make suggestions for further development of the resources.

we 1 http://kaldi-asr.org/ 2 http://laeknaromur.is/en.html 3 see e.g.


We use a hierarchical recurrent neural network (Serban et al., 2016) to model the current utterances (Figure 1a).

a recurrent neural network (RNN) captures the meaning of a sentence; another LSTM-RNN aggregates the sentence information into a fixed-size vector.


As both content and temporal provide important evidence for speaker classification, we propose to combine them by interpolating or gating mechanisms (illustrated in Figure 1d).

we have Here, g is known as a gate, balancing these two aspects.


This paper presents an approach to simulate automatic speech recognition (ASR) errors from manual transcriptions and describes how it can be used to improve the performance of spoken language understanding (SLU) systems.

we point out that this noising process is very usefull to obtain a more robust SLU system to ASR errors in case of insufficient training data or more if ASR transcriptions are not available during the training of the SLU model.


Thus we calculated informativeness scores based on the common information between the output of the MSC system and the references using ROUGE (Lin, 2004).

we used the f-measure metrics ROUGE-1, ROUGE-2 and ROUGE-SU4.


More precisely, we annotate the sentence number in the reference summary to the utterance.

we link important utterances with sentences in reference summaries that already exist in the Kyutech corpus.


Some abstractive conversation summarization approaches utilize extractive summarization techniques in the process (Banerjee et al., 2015).

extractive summarization has an important role in summary generation.


We evaluated our methods on the Kyutech corpus with nine-fold cross validation for nine conversations.

we evaluated one test conversation with the model that was generated from the other conversations and repeated this process for all conversations.


Moreover, there was almost no difference between a method using only the utterance position feature and others.

our models heavily depended on the utterance position feature.


They use the Wikipedia featured articles as source for summary texts, since they are (i) well-written, (ii) comprehensive, (iii) well-researched, (iv) neutral, and (v) stable according to the Wikipedia featured article criteria 2 .

the first section of each featured article (also called the lead section) is supposed to be a good summary of the topic according to the Wikipedia guidelines.


There are also no sharp boundaries between classes.

annotators had difficulties with annotating Inference cases.


Simpler, rule-based approaches, like the Jaccard index, are highly competitive for small data sets, but as more data becomes available, they lose out to more sophisticated approaches.

and confirming a widespread assumption in Machine Learning, deep learning approaches come into their own, and its performance surpasses all other approaches, only when a sufficiently large amount of training data is available, containing above 30,000 pairs of sentences with a 50/50 split between duplicates and nonduplicates.


In order to serve the research community, the adherence to the principles of Open Science not only demands clarity about the data services offered, but also about what is to be expected from the analysis tools offered.

researchers that do know how to couple the data they aim to use but that have not developed tools themselves, are in need of guidance and explanation of the various aspects of quality.


System infrastructure has evolved to provide ever greater redundancy, data integrity and disaster recovery.

lDC deploys ZFS as the filesystem of choice to implement continual checks against data corruption.


LDC normalized, QCed and time aligned the transcripts and worked with the FTD lab to develop automatic methods to analyze prosody correlated with clinical evaluations.

we computed fundamental frequency and log-scale pitch range controlling for individual and sex differences and correlating to neuropsychiatric tests and measures of gray matter atrophy.


Further, participants were asked to define the research fields, areas and sub-areas, methods and applications they work on.

important for this survey was to assess in which economic sectors developed applications can be used.


Here we apply an additional requirement that a complicating action must cause the tension to rise, and must make the MRE causally possible.

it must cause something to become more complicated, as the name implies.


Although epigraphs are a small part of the ecology of the novel, treated as a network they reveal fresh connections and perspectives on literature • To study the hitherto untold history of the literary epigraph and its evolution in purpose and form.

the project seeks to discover the origin of the first epigraph, to determine whether the use of epigraphs increases in frequency over time, and to contextualise its evolution in relation to a changing socio-historical context.


Nevertheless, we can calculate a kind of pseudo-recall by comparing the performance of PolNeAR and PARC3 annotators on the articles from PARC3 that were annotated by both groups.

we ask: how many of the PARC3 annotations are recalled by the PolNeAR annotators, and vice versa?


Agreement with the control annotator is generally lower across the board, One of the consequences of this preliminary study is that we are better placed to predict what a face description dataset will contain, and what the challenges for automatic face description will be.

descriptions are bound to refer to a mixture of attributes, not only physical, but also emotional.


Note that we don't use the term "valid" or "good" argument due to the inherent subjective evaluative meaning of these adjectives.

the game is structured into game rounds (an atomic mini-game in which an interaction from the user is required and is usually rewarded with points), levels (predefined sequences of game rounds, for example with increasing difficulty), and worlds (a set of interconnected levels that are visualized as a landscape in the game).


In the following sections, we give some details about how we approached this optimisation problem.

we wish to select the best set of questions from the previously collected survey data (with N being as low as possible) with the best set of prediction areas (i.e., the M areas being as small as possible), in order to achieve the highest accuracy A.


Some variants were hardly ever used or showed no geographic variation at all, so we discarded them with a single-pass feature elimination based on χ² score.

we removed those variables that were the least statistically dependent on location.


Automated machine reading can help create and validate hypotheses using these models, but there remains a disconnect -often relevant events are written in high-level language, yet the model requires specific quantities.

when describing events, authors often make use of gradable adjectives, i.e., adjectives (such as small) that can take a range of magnitudes or degrees (e.g., something can be a little small, very small, extraordinarily small, etc.)


Here we propose a method for quickly and concretely grounding a large number of gradable adjectives such that their effect can be calculated for entities or events of interest.

we gather human intuitions about the ef-fect of a particular gradable adjective on a given distribution (i.e., mean and standard deviation), independent of the item being modified, and then use this data to fit a linear model.


(2003) propose a model for finding perceptual groundings for adjectives.

they ground their adjectives using audio features and are then able to predict a lexical description of unheard music.


With this model, then, we can predict the impact of the adjective on an entity whose distribution is known.

our approach operates in two parts: gathering the human language intuitions for each adjective using crowdsourcing (described in Section 4.)


The residuals of the model (i.e., respDev ∼ 1 + adj + σ p + µ p + σ p : µ p + (1|turker)) showed that the data was heteroskedastic.

as the predicted values from the model increased, so did the magnitudes of the error residuals.


The final model is given by: This fitted model itself is our resource.

for each adjective, we have a linear function, f adj (µ p , σ p ) that describes its predicted impact of the quantity in question.


the residual error of each data point (i.e., individual response) is based on a model trained on all the data except for that point.

the predictive R 2 is the predicted residual error sum of squares (PRESS) statistic (Allen, 1974) divided by the total sum of squares (SS total ): That is, for each individual response i ∈ D, we sum the residual squared error between the true value, logRespDev i and the value predicted by a model trained on the rest of the data, logRespDev i,D\i .


While they allow us to estimate groundings for adjectives that we did not include in training, the estimates are only as good as our capacity to extract the necessary information from the embedding.

the embeddings were trained to capture distributional similarity, not relative magnitude of impact.


The alignment tool is an ASR system that is used in a forcedalignment mode.

it is used to associate words in the provided orthographic level transcription with the corresponding audio segments making use of a pronunciation lexicon which represents each word with one or more pronunciations (phonemic forms).


The sentence Jill is a person could be depicted as a unary predicate, person(Jill).

there is an absolute set of people, but there is no absolute set of friends, without first specifying the person with whom they are friends.


Relational phrases that are semantically related might consist of words with similar meaning such as "birth" and "born" in this example.

we reduce the data sparseness of relational phrases by connecting patterns and predicates through the decomposed words.


These statements serve as an auxiliary resource that aids to model the meaning of the decomposed words.

we co-train a distributed word model in the same vector space.


We further filter the mapping results with argument type constraints.

the argument type of an NL pattern should be consistent with those of its corresponding KB predicates.


We use the training corpus to learn the optimal projection.

the projection that globally minimizes the distance between the projection of terms in the ontological space and the vectors of the associated concepts is determined.


The qualities of six translations among English, Chinese, and Korean were measured using Japanese as the pivot language.

we measured the BLEU scores for (En | Zh | Ko) → Ja → (En | Zh | Ko) using the Ja ↔ (En | Zh | Ko) models trained for direct translation tasks.


Some inexact news duplicates were filtered out based on several heuristics.

for denik.cz, all regional pages were dropped based on their URLs, since they were mostly either reprints of central news or very specific entries such as "Where to play football this weekend".


As there are not large parallel corpora manually annotated with Named Entities on which to perform the projections, we annotate a large parallel corpus with already existing Named Entity taggers so that we can use the automatic Named Entity annotations and word alignments to undertake the projection across languages.

in order to automatically generate NER taggers without manual intervention via the strict-match algorithm our method goes through the following four steps: 1.


We train a simple one-layer convolutional neural network (CNN) on top of the word embeddings, which has shown to perform well on text classification tasks regardless of training data size (Kim, 2014).

convolutional filters are applied to windows of word embeddings, with a max-over-time pooling on top of them.


We investigate citation networks in terms of content distance and study how the uncovered patterns can be used for identifying highly influential publications.

we investigate the relations in a citation network studied in (Yan et al., 2012), (Knoth and Herrmannova, 2014) and (Whalen et al., 2015), which are depicted in Figure 1.


The t-test is a measure commonly used to assess whether two sets of data are statistically different from each other.

it helps to determine the features that can distinguish literature reviews from seminal papers.


These are particularly features describing the distance distributions B, C and D. The three best performing features are all related to the distance between a publication and its references.

this experiment confirmed the features describing the distance between a publication and its references distinguish between the two types of papers (F13, F15, F16).


The corpus presented in this paper contains questions from the educational environment.

we collected science questions asked by teachers in real middle school classrooms.


After a thorough analysis of the questions in our data, we propose a novel taxonomy containing 16 categories of questions based on expected answer types.

the taxonomy was created considering previously proposed schemes and the particularities of questions in our data, with the focus on the features that would facilitate the identification of what is expected from a correct student response.


The increased demand for structured knowledge has created considerable interest in relation extraction (RE) from large collections of documents.

distant supervision can be used for RE without manual annotation costs.


As the demand for structured knowledge has increased, considerable interest has emerged in relation extraction (RE) from large collections of documents written in natural language.

with "distant supervision" (DS) (Mintz et al., 2009;Hoffmann et al., 2011;Riedel et al., 2013), it is possible to extract the relationships between pairs of entities without human manual annotation using a knowledge base (KB); this heuristically aligns entities in texts to a given KB and then uses this alignment to train an RE system.


This paper introduces a dataset to evaluate natural language understanding approaches with a focus on interpretation processes requiring inference based on commonsense knowledge.

we present MCScript, a dataset for assessing the contribution of script knowledge to machine comprehension.


Since the overlap model selects the answer with a high overlap to the text, it does not perform well.

this also explains the very bad result on text-based questions.


(2016), the compared models were trained on the datasets with different size.

the neural network-based models (Lin et al., 2016) actually used a large training dataset containing 522, 611 sentences, while feature-based models (Hoffmann et al., 2011) were trained on a small dataset containing 126, 184 sentences.


In this paper, we revisit the distant supervision for relation extraction.

our contributions include: • We carefully re-examine the experimental settings of previous MIML-based work for RE.


Therefore, we constructed features (b) and (c) using phonological similarity of consonants described later.

the following processing is added to the reading Kana sequence extracted in the feature (a).


Here we discuss some of the cases where verb/location typicality can in its turn aid vision tasks.

consider the tasks of activity and scene recognition.


For this dataset we obtained on average precision and recall around 80% level, but have a large variance between dialects.

precision remains rather low for Chugoku and Kyushu, presumably due to the confusability between the dialects.


Thus, to determine which dialect should be processed in a text, it is unavoidable to use criteria related to these levels of analysis.

we mention that there are important vocabulary differences from one dialect to another.


"Listen, [female] dear, the problem lies both in you and them.

be yourself, as kind as you are, but don't put extra trust in anyone.


We test this intuition, finding it holding true as shown in Figure 3.

our data have wider lexical coverage than is captured in the (Zahran et al., 2015) model.


The ranker is however doing a slightly better job on BetaBlockers and Triptans (Tables 3b  and 3c).

when separating Y from M, the ranker is not performing much better than chance on many of the topics.


Firstly, additional datasets might be evaluated in order to provide more detailed study.

tasks such as machine translation, which do not aim to classify the input, could be studied.


Since one of our goals is to identify trolling events, our datasets will be composed of suspected trolling attempts (i.e., comments that are suspected to be trolling attempts).

some of these suspected trolling attempts will be real trolling attempts, and some of them won't.


The present study continues this line of research by relating individual terminological units to coarse-grained semantic classes.

by adding semantic class information to existing knowledge about terminological units, we enable multi-dimensional queries of the data.


We also use two additional data sets that have been created on the basis of the ACL ARC.

we use a list of technical terms (ACL RD-TEC 1.0, termed ACL1) that was created by means of automatic term extraction (Q. Zadeh and Handschuh, 2014).


Muslim women, on the other hand, are stereotyped as oppressed (Stadlbauer, 2012).

many inhabitants of non-Muslim countries believe that the veil of Muslim women (e.g., hijab, niqab, and burqa) is a representation of oppression (Wagner et al., 2012).


From a computational linguistics point of view, this study serves the purpose of exploring what microportraits have to offer for this type of investigation.

we explore whether they allow communication scientists to go beyond methods that are commonly used, such as word clouds based on Tf-idf scores.


The outcome of our use case reveals that microportraits can provide a different perspective than more basic approaches such as LDA.

our method detected severe forms of stereotyping that remained unnoticed when looking a word co-occurrence alone.


Mental and behavioral disorders, such as substance abuse, are top on the list of the most costly and prevalent conditions worldwide.

1 in the US, a recent survey on public health reported that in 2014 3.3% of all adults had co-occurring mental illness and substance abuse disorders.


Following this idea, our paper analyzes counseling conversations with the final goal of distinguishing between low and high quality counseling.

we focus our analysis on counseling conducted using Motivational Interviewing (MI), a well-established evidence-based counseling style for treating addiction and other behaviors (Moyers et al., 2009;Catley et al., 2012;Apodaca et al., 2014).


Examples of such inquiry could be the kinship (or personal) relationship between politicians and their connection to entities such as institutions, movements, events or places throughout their public life.

we aim to construct a resource able to answer questions such as "Which politicians were born before the 1960s, had military training and held a position in the Executive Branch?".


The explicit semantic nature of appositives led us to a semantic strategy for revision the parser analysis.

we extracted from the parsed sentences the triples formed by the appos relation -the linearization of the noun phrases that have their heads connected by an appos relation.


Mining and organizing the information surrounding a cultural event can help broadening the perception and visualization of its social impact.

microblogs are increasingly used in cultural events like festivals.


Contextualizing microblogs appeared to be more effective than focus retrieval approaches to link microblogs with WikiPedia pages.

instead of considering the content as a query, WikiPedia text anchors were matched against it.


Using a vocabulary of High German, we filtered out all sentences whose normalizations included words outside this vocabulary.

we kept only truly High German sentences, along with their original Swiss German counterparts, resulting in about 45,000 GSW/DE word-aligned sentence pairs.


The visibility of an omission error can be defined as the expectation of content being missing when only reading the translation (without comparing it to the source).

is the omission visible/ expected at monolingual level?


In general, for any threshold τ , we can extract a subset MLT τ of the MLT Dataset consisting only of words with ambiguity score ≥ τ .

the threshold τ can be used to regulate the difficulty of the MLT Dataset.


It assembles user activity data obtained from translation tasks into several languages using a common set of six short English source texts.

keystroke and eye tracking data were recorded during from-scratch translation sessions and during post-editing of machine translations.


(2016b) provide evidence that HTra is higher when the target language is more remote from the English source language.

hTra is about double for English>Japanese than for English> Spanish when the same source texts are used.


(2016b) provide evidence that |CrossS| values are higher when the target language is more syntactically remote from the source language.

|CrossS| values are almost double for English>Japanese than for English>Spanish when the same source texts are used.


It is notable that the shorter pauses, which we have argued are likely to be monitoring pauses, contribute significantly to the pause-word ratio for both language pairs.

it appears that a considerable amount of the cognitive effort expended in translating from scratch is devoted to monitoring what is being produced.


As with translation from scratch, it is notable that the shorter pauses, presumed to be monitoring pauses, contribute significantly to the pause-word ratio for both language pairs.

it appears that a considerable amount of the cognitive effort expended in post-editing is devoted to monitoring what is being produced.


The inserted parallel sentence pairs should not be trivially detectable in the monolingual corpora.

these sentences should be coherent with the context in which they are inserted.


In what follows, we will focus on these important issues from a neuro-computational perspective.

we are interested in evaluating the net effect of the complexity of an inflectional system on the processing behaviour of a recurrent neural network, excluding the role of word token frequency effects on prediction-driven processing (Pickering and Garrod, 2013).


The complex interaction between entrenchment and competition in a TSOM accounts for important dynamic effects of self-organisation of stored words (Marzi et al., 2014;Marzi et al., 2016).

at a sublexical level, systematically recurrent patterns tend to recruit contextsensitive specialised (and stronger) chains of BMUs.


For all models/languages, we treated TSOM instances, verb paradigms and word forms as random effects.

we show how inflectional systems of different complexity (independent variables) affect TSOM processing, by focusing on symbol prediction rate as a dependent variable.


Oromo morphology, on the other hand, does not pose such theoretical challenges, so we will instead use this section to illustrate the practical benefits of our parser combinator framework.

even for a case (such as Oromo) where the increased expressive capabilities of parser combinators are not a benefit, we show how the ease of implementation of parser combinators can make them a good choice for implementing morphological parsing under time constraints.


These are bounded from above by SFType, and our improvement needs and issues-measured by mean F1 and occurrenceweighted F1, which differ according to whether or not a situation frame is given greater weight when multiple annotators have annotated it.

(to say, if only one annotator detects an evacuation need in a document, it counts less towards occurrence-weighted F1 than if all annotators detected it.)


We would like to see how well our methods work on compound words it has not seen before.

we evaluate our compounding methods on two tasks: 1. f2e: Given a foreign compound word, can we decompose and translate it into English?


For the e2f task, we were able to successfully recover 87 of the 100 test words.

after removing the test word from the dictionary, the model was able to reconstruct the translation of the compound word only from its components, because other languages used the same components in the compounding recipe, either in direct association or via previously unobserved derived associations via the Cartesian product and/or reordering models.


In our discussion, we show some future directions for morphological treebanks.

we aim at the combination with other reliable lexical resources such as GermaNet.


This sometimes results in low scores (no match) by human evaluation of isolated sentence pairs, although all simple sentences that correspond to the same original sentence, when seen together, perfectly match the original sentence (Table 5).

when treated separately, some of the sentences aligned between levels 0 and 4 present false negatives, as they will later be merged together for training ATS systems, where they will then represent good training material for sentence splitting.


Since the size of the GA training data was limited and due to the high overlap between GA and MSA, we opted to augment our data with MSA data.

we used a set of 20,000 tweets, which were gold-segmented into about 890,000 segments and we tagged them using Farasa.


This would mean less tokens remaining -or less readings for a remaining tokenat the post-CG disambiguation stage, where again there is room for further development.

our coverage dictionary could be redesigned or improved, any additional techniques for discerning the gender of proper nouns explored, and new disambiguation steps explored and developed, in order to handle more of those tokens that cannot be pruned by CG-formatted rules alone and to further increase the standard to which we can POS tag Welsh sentences in context.


In the recent past, supervised learning methods have produced high accuracies for Parts-of-Speech (POS) tagging (Gimenez and Marquez, 2004).

sequence models such as hidden Markov models (HMM) and conditional random fields (CRF) have given good results (Huang et al., 2015).


The cognitive framework Fearnot AffecTIve Mind Architecture (FAtiMA) (Dias and Paiva, 2005) was used to achieve that.

the modular version presented in (Dias et al., 2014).


The classifiers were trained using pairs of statements in four argumentation structures written in the same language then were tested using pairs of statements in the other argumentation structure.

for training in CBOW, LSTM, and BLSTM, training data were divided into two parts; for training (90%) and development to tune the parameters (10%).


Next, we compare our collected dialogues to the single-turn dialogue descriptions described in Section 4.).

we focus on the "agent" turns of our dialogues, as they are more directly comparable to the property and room turns.


Reflections are statements that define therapeutic presumptions about what the client is trying to express about their inner experience (Miller et al., 2002).

counselor reflections are the thoughts, feelings, and meaning of what a client said.


This gives us an idea of the relative time location of the peak within a time segment (in this case a word).

the computed minmax score provides information on the location of the F0 peak ('maxF0.t') and intensity valley ('minint.t').


Our embodied actions in conversations are usually coupled with environmental elements (Streek et al., 2011).

many of our gestures are coupled environmentally (Goodwin, 2007).


Our analysis in section 5 identified the following interactional practices.

the SCs and visitors were able to proceed in concert to the next exhibit while maintaining their conversation by engaging in the following behaviors in a step-wise fashion.


Within each run, the scores are the prevalence-weighted macroaverages across classes.

the scores are averaged over all classes and weighted by their frequencies.


for this corpus, we distinguish the subclasses: Facts, Principles of Law and Case-law.


However, as the vocabulary size increases, the deviation of the vocabulary commonality index gradually becomes smaller.

children who have the same vocabulary size tend to produce common words with the same ratio.


tscore, log-likelihood) are used, so that the dimension of the corpus and the particularly high frequency of some tokens (like articles, prepositions) do not affect the results.

the following kinds of multiword lexicon and word collocations have been investigated up to now: multiword nouns with structure Noun Prep Noun (i.e.


Children have been shown to rely on such distributional information for syntactic categorization from around 12 to 16 months (Höhle et al., 2004;Mintz, 2006).

german 16 month-olds use determiners to correctly categorize words as nouns even when the kind of determiner was different during familiarization and test (Höhle et al., 2004), making it necessary to generalize.


1 Clearly the frequency in which children hear a particular form is a function of synthesis combinatorics (Stoll et al., 2017).

in languages where morphology is in a closer one-to-one relationship between word and grammatical function, these forms will occur more frequently in 1 Another example is verbal inflection: English typically has four forms, e.g.


Although there exist Mi'kmaq dictionaries (Rand, 1888;DeBlois, 1996) and translated texts (DeBlois, 1990), Mi'kmaq remains a low-resource language.

no (large) machine-readable corpora are available for Mi'kmaq.


Because Mi'kmaq is a polysynthetic language, we consider character-level and subwordlevel language models, in addition to more-conventional (word) n-gram models.

we consider the following approaches to language modelling: KenLM We use KenLM (Heafield et al., 2013) to build conventional (word) n-gram language models with modified Kneser-Ney smoothing, for differing orders of n. char-rnn We consider a character-level LSTM language model.


Specifically we consider the following approaches to language modelling: KenLM We use KenLM (Heafield et al., 2013) to build conventional (word) n-gram language models with modified Kneser-Ney smoothing, for differing orders of n. char-rnn We consider a character-level LSTM language model.

we use a TensorFlow implementation 13 of char-rnn, 14 with its default parameter settings.


In order to apply a grapheme-to-phoneme system to nonstandard text a first obvious condition is to have such a system at disposal.

for low-resource languages, which also present high levels of spelling variation, the development of grapheme-to-phoneme systems is hindered by the lack of complete studies about the phonological system as well as the absence or the small volume of existing pronunciation dictionaries (word-pronunciation pairs).


The results of this experiment are presented on the Table  7.

extractive summaries show disproportionate results compared to the actual polarity of the corpus.


We considered as starting point for this work the scheme provided in (Karoui et al., 2017), which was initially applied to a set of 400 Italian tweets.

we would like to highlight how the complexity of a pragmatic device such as irony, also attested in literature (Grice, 1975;Grice, 1978;Sperber and Wilson, 1981;Wilson and Sperber, 2007;Reyes et al., 2010;Fink et al., 2011;Reyes et al., 2012), makes the annotation task particularly challenging, as will be discussed in a deep analysis of the disagreement between the native Italian speakers involved in the development of the resource.


Some Italian corpora containing Twitter data, where the presence of irony is marked, have been made available to the community in the last few years, thus we extracted from them the new 1,200 tweets to be included in TWITTIRÒ.

the tweets were collected from the following three different pre-existent datasets: TW-SPINO, TW-SENTIPOLC14 and TW-BS.


The figure of speech is referred to the fact that the world-known footballer once affirmed that, if his father tells him to sign something, he would do it without hesitation.

the athlete has signed (apparently without knowing) some contracts regarding the rights on his public image, and money has been transferred in fiscal paradises such as Uruguay and Belize.


The work of arriving at a set of 105 test items (100 main items plus five practice items) was carried out by a team of Deaf and hearing sign language researchers and involved both excluding certain categories of signs, similar to what had previously been done for a lexical comparison study involving DSGS (Ebling et al., 2015), and prioritizing specific signs.

signs denoting persons (e.g., CHARLIE-CHAPLIN), organizations (e.g., GAL-LAUDET), places (e.g., AUSTRALIEN 'AUSTRALIA'), and languages (e.g., DEUTSCH 'GERMAN') were excluded.


Though such studies have been largely limited to spoken languages, promising results have been documented once similar methodologies are applied to sign languages (Woodward, 2000;McKee and Kennedy, 2000).

woodward (2000) proposes a sign language (SL) adaptation of the Swadesh list.


Woodward (1993) adapted the original Swadesh list for the purpose of sign language comparison (Figure 1).

he removed body parts and pronouns because they are often represented in SLs by pointing to the referent; thus, they may lead to an overestimation of the relationship between SLs.


However, none of them has been explicit on how similarity is measured.

for each class of phonemes it was never specified the set of contrastive features that would determine a significant difference between any two phonemes.


Table 2 reports the results of the comparison made with the full set of phonological features; Table 3 reports the results after handshapes with the same selected fingers but different joint flection have been collapsed into one level; Table 4 reports results after place of articulation has been collapsed into five major regions (neutral space, head, torso, arm, hand), while    14.89% 77.66% Table 5: After collapsing locations and handshape some similarities emerge.

table 5 shows that CSL and HKSL share around 40% of the signs in the Woodward list and should be treated as two different languages of the same family.


In this paper, we address collection of prepared Sign Language discourse, as opposed to spontaneous signing.

we aim at collecting long discourse, which creates problems explained in the paper.


Avoiding disfluency requires prepared discourse, as opposed to spontaneous.

the final production is intended to match a planned result, not built on the fly.


To describe the linguistic organization of SL, we adopt an approach that does not assume the formal requirements of linear models.

no hypothesis or prediction of a linguistic functioning is advanced beforehand.


Each of the rules identified in this study depends on a nesting of arguments to generate a language.

their production requires at least a level of nesting.


Also, it is suggested that labels should be interpreted in context, notably in conjunction with the discourse mode.

the expressive strategy implemented in the corpus narration is very specific, conveying almost continuously positive valence but in a subtle way, through pitch modulation and with focus words.


By this way, our results are comparable with those obtained on other existing emotion corpora.

emotional models are trained in cross-validation conditions (here 5 folds to keep enough data) on acoustic features.


Speech segments and music segments may overlap and are maximally long and maximally inclusive.

if the recording contains continuous speech for 15 seconds with no noticeable pauses, annotators created a segment on the speech track whose duration is 15 seconds -even if the number of speakers or the languages spoken changed during that segment.


those forms which are subject to morphological modifications) is partially dropped in LICO.

the resource does not include forms which exhibit morphological inflection or conjugation, but includes connectives which show a certain degree of lexical variability, that is, multiword expressions which are not totally rigid from a lexical point of view (e.g.


To reach our goal, we extend the search to other documents.

on one hand, we con- 6 Arg2 is the argument that is syntactically bound to the connective, and Arg1, the other one (Prasad et al., 2007 Concession:Arg2-as-denier examples id="1" source = "CIB 405635 185,186" text: "La nostra idea -dice Rita -è quella di aprire un bar normale.


CIB, Adige, Wikipedia) have been reported 8 Documents are from Italian Wikipedia, February 2010. in LICO.

the examples have their own id and are identified with the tag "source" which attribute is a two or three position code standing for: i) the source corpus (i.e.


A complementary investigation has been conducted picking examples of the connectives in corpora and manually disambiguation their senses in the retrieved textual contexts.

this latter strategy is replicable to enrich LICO with information about discourse connectives that convey relations other than contrast (e.g.


(2017) use parallel corpus to discover correspondences between connectives in different language and highlight point to gaps in the examined resources.

the authors report on experiments to validate the list of connectives in Dim-Lex (Stede, 2002) and LICO in an effort to constructing a bilingual lexicon on connectives that are connected via their discourse senses.


We define lexical simplifications as any word and phrase replacements that make the sentence more easily 2 https://github.com/SIMPATICOProject/ simpa 3 https://www.sheffield.gov.uk understood.

a lexical simplification modifies the sentence locally, without altering sentence grammaticality or compromising meaning.


In this work, we start with a discussion of resources created from the web (Section 2).

for Brazilian Portuguese, we describe how starting with a 1.6 billion token corpus  and following a strict methodology (Section 3), the resulting corpus is expanded to encompass 2.7 billion tokens.


As for the morphological data, the relationship that holds between affixes could be 11 http://www.w3.org/ns/lemon/vartrans# 12 To examine the increased expressivity, please compare an example of the source and RDF data here: http://mmoon.org/ lrec2018figures/ 13 http://rdf.corpora.uni-leipzig.de/ resources/xho/inventory/lexeme_umbiki_n 14 http://rdf.corpora.uni-leipzig.de/ resources/xho/inventory/lexeme_ababiki_n further specified by making use of the two object properties blm:isAllomorphTo and blm:isHomonymTo.

figure 2 shows that the prefixes abaand abare allomorphs to each other since they share the same meaning (noun class 2 and plural) but differ in their orthographic representation.


In this case, the algorithm will aim at obtaining t v + ≥ w uv t u ≥ w 2 uv t v − w uv to the extent possible.

it will try to keep the two nodes close to each other, aiming at placing t u in the interval wuv (t v + ) and similarly for s u , s v .


We put significant efforts to achieve high quality LCM labeling.

this rules out purely automatic verb labeling, at least when applied as reported in (Rogozinska and Wawer, 2013) due to low precision.


However, there are many open issues which may cause confusions for both human annotators and systems.

for low-resource languages like Uyghur, it's also challenging to train native speakers to follow a long annotation guideline (usually more than 20 pages) which may still leave many language-specific issues underspecified or unresolved.


Its development was supported by two datasets: a) a sizable unannotated dataset of Persian sentences for training word embeddings, and b) an entityannotated dataset for training named-entity classifiers.

this paper makes three distinct contributions: 1) it officially releases the entity-annotated dataset with an ISLRN 2 that should make its utilisation easier; 2) it releases four different word embeddings trained on the unannotated 1 Western/Iranian Persian which is also known as Farsi.


For the targeted applications of supply chain monitoring and personal travel planning, we are interested in the annotation of fine-grained geo-locations, such as street names and public transport stops, as well as relation (or event) mentions with typically multiple arguments.

we are not only interested to know that a given event type occurred, such as a traffic jam, but also, on which road, between which exits, and what the resulting time delay is for drivers.


Optional arguments from the gold relation mention that are not contained in the predicted relation mention do not count as errors.

we count a predicted relation mention as correct if it contains all required arguments and is subsumed by or equal to the gold relation mention.


Tweets are much more representative of the data we see from users in an interaction with a social bot than newswire data.

utterances tend to be short, and due to the open domain setting, relevant context is not guaranteed.


Table 7 demonstrates two areas in which adding an additional layer of NLU would yield increased performance.

we notice in Sample 7b that abbreviations cause difficulty in classifying entities.


There are clearly many areas in which we can further evaluate and improve the performance of SlugNERDS.

we plan to evaluate the performance of SlugNERDS with an emphasis on the impact of contextual information provided as a result of our dialogue system's state tracking capabilities.


In this work, we analyze to what extent transfer learning may address this issue.

we demonstrate that transferring an ANN model trained on a large labeled dataset to another dataset with a limited number of labels improves upon the state-of-the-art results on two different datasets for patient note de-identification.


The successes of ANNs for many applications over the last few years have escalated the interest in studying transfer learning for ANNs.

much work has been done for computer vision (Yosinski et al., 2014;Oquab et al., 2014;Zeiler and Fergus, 2014).


We thus provide a CoNLL-TSV-based enrichment pipeline that includes the chunker in addition to other annotators in order to assess the impact of, for example, semantic factors such as animacy on word order alternation.

this allows to verify existing hypotheses established by traditional qualitative research in the philologies by means of quantitative methods.


In consequence, we obtain an isomorphic representation of the original CoNLL data structure in RDF which is semantically shallow, 8 but can be effectively queried, manipulated and serialized back into CoNLL using off-the-shelf RDF technology.

this includes a rich infrastructure of databases, webservices, APIs, models for resource publication and linking (Chiarcos et al., 2013).


Early and aggressive updates of the perceptron are performed at training time.

following ideas from dynamic oracles (Goldberg and Nivre, 2012), updates may occur for actions that clearly results in violations of the gold tree, for instance when adding a bad dependency.


The paper presents a new methodology aimed at acquiring typological evidence from "gold" treebanks for different languages.

it investigates whether and to what extent algorithms developed for assessing the plausibility of automatically produced syntactic annotations could contribute to shed light on key issues of the linguistic typological literature.


Within this area of research, the paper reports the results of preliminary experiments aimed at acquiring quantitative typological evidence from a selection of the UD treebanks for different languages.

it focuses on the widely investigated topic of word order, with the specific aim of reconstructing word order patterns within and across languages, for what concerns both the linear order of words and its degree of flexibility (giving rise to a wide typology of languages going from fixed-order to free word order languages).


For the purposes of the present study, LISCA has been used in its de-lexicalized version in order to abstract away from variation resulting from lexical effects.

two different types of features are considered: • local features, corresponding to the characteristics of the syntactic arc considered, such as the distance in terms of tokens between d and h, or the associative strength linking the grammatical categories (i.e.


In this paper we presented a new methodology aimed at acquiring typological evidence from "gold" treebanks for different languages.

we investigated whether algorithms for measuring the plausibility of dependency relations within the output of dependency parsers could be exploited to acquire quantitative evidence from gold treebanks to shed new light in linguistic typological studies.


By analysing our experimentation results, we can conclude that our proposal enables a sophisticated cross-checking of the information in WordNet, SUMO and their mapping.

by means of practical examples, we have illustrated that the proposed system enables (a) the validation of some pieces of knowledge and (b) the detection of inconsistencies and missing knowledge.


As for compositional noun phrases, if the translation preserves normal word order, then the PicmreQuest-internal noun phrase recognition will take effect.

ifjeune fille translates as young girl, then PictureQuest will understand that young is an adjective modifying girl.


It should be noted that the set of domain-specific keywords and phrases was provided to the speech recognition system as a text document.

the training was not done by a human speaking the keywords and phrases into the speech recognition system.


The goal of our evaluation was to ensure that the system helped a user successfully identify parts irrespective of the performance of the speech recognition engine for the user.

we wanted to see if the system was robust enough to conduct transactions with a diverse mix of users.


The TDMT system for English-to-Japanese at the time Of the experiment had about 1,500 transfer rules and 8,000 transfer dictionary entries.

this TDMT system was capable of translating 8,000 English words into Japanese words.


Bratman's key insight is that human beings tend to follow a plan once they have one, although they are capable of dropping an intention or changing a partial plan when necessary.

human beings do not decide what to do from scratch at each turn.


Finally, the conversation is in a certain sense the trace of the plan.

we care much more about the actions generated by the planner than the states involved, whether implicitly or explicitly specified.


S: (draws acceleration vector correctly) Figure 5: Example of generated dialogue operators further, we must investigate more general methods of handling student errors.

we plan to investigate error-classifying predicates that apply to more than one question as well as the use of intention-based predicates.


Nevertheless, our tree transduction-based approach has some important limitations.

the framework requires the developer of the transformation rules to maintain them and specify the order in which the rules must be applied.


A signifigant additional problem is that changes need not be monotonic.

when changing his mind, or correcting the system's misinterpretations, the user may delete subtask structures altogether, as in the subdialog: S: What day are you returning to Chicago?


Only "high-precision" rules are currently applied to selected types of anaphora.

we resolve only those cases of anaphora whose antecedents the module can identify with high confidence.


It is also interesting to note that for 8 of the 12 relation types where the F-Measure dropped more than 10 points, the training set includes less than 20 instances.

there seems to be a natural correlation between low number of instances in the training set and low performance in the blind set.


As discussed in Section 3.1 and 3.2, accurate co-reference resolution is crucial to improving the accuracy of extraction, both in terms of recall and precision.

we identified two types of high-payoff coreference resolution: • definite noun phrase resolution, especially plural noun phrases • 3 rd person neutral pronouns "it" and "they."


DP ratings were significantly correlated with the ratings provided by the two human raters who agreed well (_r = 0.32 and 0.31), resulting in agreement of ratings in 63% and 66% of the questions.

the agreement of ratings provided by the system and by two human raters is comparable to the highest agreement rate achieved between the human raters.


Based on the first results, we made a few modifications and then reevaluated DP.

we added items to the possession exception list based on the new corpus and made some of the no-presupposition rules more specific.


Since the speech processing units are not in fact physically connected to the PSA we envisage that they could also be used to control or monitor other environmental functions.

our simulation allows voice access to the current and past values of the fixed sensor readings.


For a call center, this may be a difficult task to achieve, espescially under severe time pressure, but it will pay off.

all new categories should be added, outdated ones should be removed, and redundant ones merged.


We evaluate the system on the TREC8 QA development corpus as well as the TREC8 QA test corpus.

all parameters for the final QA system are determined using the development corpus.


Nevertheless, it is the summarization component that allows the linguistic filters to focus on relevant passages.

we find that very weak linguistic knowledge can offer substantial improvements over purely IR-based techniques for question answering, especially when smoothly integrated with the statistical preferences computed by the IR subsystems.


Nevertheless, it is somewhat surprising that very weak syntactic information (the NP filter) and weak semantic class information (question type checking) can produce such improvements.

it appears that it is reasonable to rely implicitly on the IR subsystems to enforce the other linguistic relationships specified in the query (e.g.


As described above, our final version of the QA system ranks summary extracts according to both their vector space similarity to the question as well as linguistic evidence that the answer lies within the extract.

each summary extract E for question q is ranked according to a new score, Sq: The intuition behind the new score is to prefer summary extracts that exhibit the same linguistic relationships as the question (as indicated by LRq) and to give more weight (as indicated by w) to linguistic relationship matches in extracts from higher-ranked documents.


Unlike in English, an eojeol is not one word but composed of a series of words (content words and functional words).

since an eojeol can often contain more than one noun, we cannot get proper interpretation of the sentence or phrase without its accurate segmentation.


In this sense, min-max is the appropriate operation for the selection.

rain value is selected between two sequences of syllables, and then max is taken from min values selected.


It must be noted here that each noun in compound nouns could be easily segmented by human in many cases because it has a prominent figure in the sense that it is a frequently used word and so familiar with him.

nouns prominent in documents can be defined as frequently occurred ones, which we call distinct nouns.


Thus, there are not many such nouns in the simple nouns extracted by the manually corrected nouns (a set of distinct nouns).

since many nouns of more 2At the mid-level of part of speech classification, for example, endings and postpositions are represented just by one tag e.g.


As the length of a sequence of syllables is shorter in Korean, it occurs more frequently.

the shorter part usually have higher frequency than the other (longer) part when we divide syllables into two.


In the initialization step, each cell of the table is assigned Word value for a sequence of syllables sj ... sj+i+l using its frequency if it is found in SND.

if the value of Word for the sequence in each cell is greater than zero, the syllables might be as a noun a part of a compound noun and so the value is recorded as MLS.


For the first issue, we use the set of distinct nouns.

the offset pointer is stored in the initialization step as well as frequency of each noun in compound noun is recorded in the table.


of experiments was performed where speciMization was inhibited for certain rule schemata.

for each grammar, the two rule schemata that received the largest number of distinct expansions in the corpora were determined.


Comparing this figures to the number of disjuncts in grammars pruned on the full corpus (516 and 388), we find that after training on nine tenths of the corpus, adding the last tenth still leads to an increase of 3-4% in the size of the resulting grammars.

the marginal gain of further training examples is still significant after considering about 900 sentences, indicating that the training corpora are somewhat too small.


This situation is ideal, since the problems we discussed in Section 2 do not exist.

all the sentences do not have segmentation ambiguity.


The morpho-fragments (MFs) of a language is defined as the smallest set of strings of the alphabet which can compose all lexemes in the dictionary.

mFs are intermediate units between   Figure 1 and Figure 2).


Therefore, a morphological analyzer with CPSbased dictionary look-up for non-segmented languages can be used for the analysis of segmented languages.

mFs make possible language independent morphological analysis.


VGroup(_ finite Tense) -> V(_ finite:4 Tense) V(_ finite:4 _) "Sequence of two finite verbs":4 A weight of 4 is attached to the rule as a whole, but there are also weights attached to the 'finiteness' feature on the daughters: their function is to make it costly for the system to apply the rule to non-finite forms.

the feature specification 'finite' is made difficult to override to ensure that it is indeed a sequence of finite verbal forms the rule applies to and flags.


Only 13 false flags are due to wrong grammar analyses caused either by the fragment approach or by the grammar's limited coverage.

genitive phrases, which are not treated at the moment, are responsible for most of these false alarms.


In conclusion, we consider the results obtained so far promising, and the problems revealed by the evaluation tractable within the current system design.

future development should focus on treating stylistic matters such as capitalisation and punctuation which have not been in focus in the current prototype.


This paper will describe some experiments using several versions of this system.

it will focus on the role that the linguistic knowledge sources play in its operation.


There may be potential for improved performance through more sophisticated use of NLP techniques.

the syntactic context in which a particular entity appears may provide important information, but it is not currently used by the system.


It should be noted that because the data are transcriptions of speech, no version of the corpus contains all the textual clues that would appear in newspaper text like the MUC-7 New York Times data.

numbers are written out in words as they would be spoken, not represented using digits, and abbreviations such as "Dr.", "Jr." or "Sept." are expanded out to their full spoken word.


The name model does not use information concerning how often each name occurs.

(it implicitly assumes that all names occur equally often.)


The coreference model is more complicated.

here we estimate for each name the program identifies zero or one possible antecedent name.


We avoid these estimation problems by reducing the parameter space.

we make the following independence assumptions: We assume that the likelihood of an adverb modifying a verb is independent of the verb's arguments (see (7)).


The examples in Table 1 may not be entirely representative of the types of polysemous adjective-noun combinations occurring in unrestricted text since they are taken from linguistic texts where emphasis is given on explaining polysemy with examples that straightforwardly illustrate it.

the adjective-noun combinations in Table 1 may be too easy for the model to handle.


In order to obtain the frequency f (a, v) the adjective was mapped to its corresponding adverb.

good was mapped to good and well, fast to fast, easy to easily, hard to hard, right to rightly and right, safe to safely and safe, slow to slowly and slow and wrong to wrongly and wrong.


We first performed an analysis of variance (ANOVA) to determine whether there is a relation between the paraphrases derived by the model and their perceived likelihood.

we tested the hypothesis that meanings assigned high probabilities by the model are perceived as better paraphrases by the subjects and correspondingly that meanings with low probabilities are perceived as worse paraphrases.


The elicited judgments can be further used to derive  Table 6: Log-transformed model-derived and subjectbased argument preferences for polysemous adjectives the grammatical function preferences (i.e., subject or object) for a given adjective.

we can determine which is the preferred interpretation for individual adjectives and compare these preferences against the ones produced by our model.


The cases in which the existence of a WordNet relation influences the value of the similarity score account for less than 10% of the cognate pairs.

instances of meronymy between cognates are very rare.


The other properties justify the use of decision trees as a knowledge representation format.

decision trees can be converted into implicational rules that an expert could inspect and can in principle be compiled back into finite-state machines (Sproat and Riley, 1996), although that would re-introduce the original efficiency problems.


These words are removed from the string given to the second pass, an already existing statistical parser trained on a transcribed speech corpus.

(all of the research in this paper was performed on the parsed "Switchboard" corpus as provided by the Linguistic Data Consortium.)


2 Each feature F i de-fines an associated random boolean variable where (X=x) takes the value 1 if X = x and 0 otherwise.

our classifier estimates a feature weight α i for each feature F i , that is used to define the prediction variable Z: The prediction made by the classifier is sign(Z) = Z/|Z|, i.e., −1 or +1 depending on the sign of Z.


The estimates of the ratios for the two S[+R] rules are obtained by counting the proportion of subject relatives among all relatives in the Treebank's parsed Brown corpus 7 .

(3) relative clauses in the Treebank are analyzed as NP → NP SBAR (rule 1) SBAR → WHNP S (rule 2) where the S contains a trace *T* coindexed with the WHNP.


Some researchers begin with some initial humanlabeled source from which they induce other morphological components.

xu and Croft (1998) use word context derived from a corpus to refine Porter stemmer output.


cut-off also helps each of the algorithms to obtain Given these constraints, suppose now there is a stronger statistical information on the words they do transitive relation from X to Z by way of some process which means that any observed failures intermediate path ={Y Y Y }.

assume cannot be attributed to weak statistics.


These techniques take a strategy that maximizes the margin between critical samples and the separating hyperplane.

sVMs achieve high generalization even with training data of a very high dimension.


SVMs find the separating hyperplane which maximizes its margin.

two dashed lines and margin (b ) can be expressed as: .


Precisely, two dashed lines and margin (b ) can be expressed as: .

this problem becomes equivalent to solving the following optimization problem: The training samples which lie on either of two dashed lines are called support vectors.


Developing dialogue systems is a complex process.

designing efficient dialogue management strategies is often difficult as there are no precise guidelines to develop them and no sure test to validate them.


However, reinforcement learning suffers from the fact that it is state based.

the optimal strategy is expressed as a decision table specifying which action to take in each specific state.


the SHIFT transitions according to a certain parameterization) extracted from the correct parse trees of a parsed test corpus.

the CPPL is an underbound of the PPL in that it would be the PPL from an ideal parser.


The recognition training phase is an iterative procedure in which a phone n-gram model is refined successively: The phone strings resulting from the current pass over the speech files are used to construct the phone ngram model for the next iteration.

this is a "Viterbi re-estimation" or "1-best re-estimation" process.


A key characteristic of this approach is that potential answers, such as person names, locations, and dates, in the corpus are predictively annotated.

the corpus is indexed not only with keywords, as is typical for most search engines, but also with the semantic classes of these pre-identified potential answers.


In this paper, we introduced a multi-strategy and multisource approach to question answering that enables combination of answering agents adopting different strategies and consulting multiple knowledge sources.

we focused on two answering agents, one adopting a knowledge-based approach and one using statistical methods.


9 and S start at the bottom left corner of the parse tree.

each letter is predicted based on the entire preceding column, and the column probability is built bottom-up based on a trigram model, considering both the child and the left sibling in the grid.


While efficient and suitable for recognition search, this column bigram FST preserves the ability to generalize to OOV data from observations made at training.

despite having been trained on a finite corpus, it is capable of creatively licensing OOV words with non-zero probabilities.


Here we seek to partially address this problem by looking at some particular aspect of clarification dialogues in the context of open domain question answering.

we examine the problem of recognizing that a clarification dialogue is occurring, i.e.


The TREC-2001 QA track included a "context" task which aimed at testing systems' ability to track context through a series of questions (Voorhees 2002).

systems were required to respond correctly to a kind of clarification dialogue in which a full understanding of questions depended on an understanding of previous questions.


Finally the results given by the system were compared to the manually recognized clarification dialogue tags.

the algorithm was evaluated for its capacity to: • recognize a new series of questions (i.e.


repetitions which refer to an identical object; or there is a strong sentence similarity between the current question and the previously asked questions.

: 1. q 0 has pronoun and possessive adjective references to q -1 ..q -n 2. q 0 does not contain any verbs 3. q 0 has repetition of common or proper nouns in q -1 ..q -n or q 0 has a strong semantic similarity to some q ∈ q -1 ..q -n A major part of our clarification dialogue recognition algorithm is the sentence similarity metric which looks at the similarity in meaning between the current question and previous questions.


Our sentence similarity measure followed on these ideas, adding to the use of WordNet relations, part-ofspeech information, compound noun and word frequency information.

sentence similarity was considered as a function which took as arguments a sentence s 1 and a second sentence s 2 and returned a value representing the semantic relevance of s 1 in respect of s 2 in the context of knowledge B, i.e.


The similarity between each word in the question and each word in the answer was then calculated and the sum of the closest matches gave the overall similarity.

given two sets Q and P, where Q={qw 1 ,qw 2 ,…,qw n } and P={pw 1 ,pw 2 ,…,pw m }, the similarity between Q and P is given by 1<p<n Argmax m similarity( qw p , pw m ) The function similarity( w 1 , w 2 ) maps the stems of the two words w 1 and w 2 to a similarity measure m representing how semantically related the two words are; similarity( w i , w j )< similarity( w i , w k ) represents the fact that the word w j is less semantically related than w k in respect to the word w i .


In other words, given two sets Q and P, where Q={qw 1 ,qw 2 ,…,qw n } and P={pw 1 ,pw 2 ,…,pw m }, the similarity between Q and P is given by 1<p<n Argmax m similarity( qw p , pw m ) The function similarity( w 1 , w 2 ) maps the stems of the two words w 1 and w 2 to a similarity measure m representing how semantically related the two words are; similarity( w i , w j )< similarity( w i , w k ) represents the fact that the word w j is less semantically related than w k in respect to the word w i .

similarity=0 if two words are not at all semantically related and similarity=1 if the words are the same.


• The remaining 31% could not be answered by making reference to the previously retrieved documents, and to find an answer a different approach had to be taken.

: • 6% could be answered after retrieving documents simply by using the words in the question as search terms (e.g.


Mohri's pushing strategy-we will see others-is always to extract some sort of "maximum left factor" from each suffix function F q and push it backwards.

he expresses F q = k ⊗ G for as "large" a k ∈ K as possible-a maximal common prefix-then pushes factor k back out of the suffix function so that it is counted earlier on paths through q (i.e., before reaching q).


If M has an arc q a: The main difficulty in completing the construction of M is to ensure each weight k exists.

f [r] must be carefully chosen to be a residue not only of f r1 , .


The strategy also requires (K, ⊗) to be left cancellative, i.e., k ⊗ m = k ⊗ m implies m = m (if k = 0).

left quotients by k are unique when they exist (except for 0\0).


12 Alternatively, Mohri (2000, §4.5) explains how to temporarily eliminate final weights before the merging step.

13 λ(Fq)\Fq(γ) exists for each γ ∈ Σ * .


Their values are as if the original pushing had pushed back not λ(F qi ) ∈ ∆ * but only its shorter prefixλ(q i ) def = λ(F qi )/ρ(q i ) ∈ ∆ * (note the right quotient).

an arc from q i to r i with weight k ∈ ∆ * was reweighted asλ(q i )\(k ⊗λ(r i )).


At the end of each iteration, only the single best improvement is executed; all others, even when independent, are discarded.

the algorithm needs one search iteration per improvement.


For that purpose we augmented the system's lexicon with specific concept mappings.

for each entry in the lexicon either zero, one or many corresponding concepts where added.


The best results are achieved with the @ ¡ £ ¥ ¤ ¡ .

the proportion of concepts vs. words must be no less than 1 to 3.


Let X(x) be the set of clues for determining the sense of a first-language target word x.

x(x)={x'|(x, x')∈R x }, where R x denotes the collection of pairs of related words extracted from a corpus of the first language.


Furthermore, let Y(x, x'(j)) be the set consisting of all second-language counterparts of a first-language pair of related words x and x'(j).

y(x, x'(j)) = {(y, y') | (y, y')∈R y , (x, y)∈D, (x'(j), y')∈D}, where R y denotes the collection of pairs of related words extracted from a corpus of the second language, and D denotes a bilingual dictionary, i.e., a collection of pairs consisting of a first-language word and a second-language word that are translations of one another.


First, a sense-vs.-clue correlation matrix is calculated for the full set of senses.

the resulting correlation matrix is denoted as C. C(i, j) is the correlation between the i-th sense S(x,i) of a target word x and its j-th clue x'(j).


The correlation matrix calculated for the set of senses excluding the k-th sense is denoted as C -k .

c -k (i, j) (i≠k) is the correlation between the i-th sense and the j-th clue that is calculated excluding the k-th sense.


The set of senses is updated by merging every pair of mutually most-similar senses into one.

the σ is a predetermined threshold for similarity, which is introduced to avoid noisy pairs of senses being merged.


One of these is that it produces a corpus-dependent inventory of word senses.

the resulting inventory covers most senses relevant to a domain, while it excludes senses irrelevant to the domain.


Let c(e, s) be the context of e in s. Let σ be a summary function of contexts.

we can then use the context summary estimate: we return the exact Viterbi outside score for some context, generally not the actual context, whose summary matches the actual one's summary.


With our decoder, translating 1755 sentence of length 5-15 words takes about 10 minutes on a 2 GHz Linux system.

we achieved fast decoding, while ensuring high quality.


The following example illustrates how all these pieces are put together to generate answer proofs.

a program called Mosaic , developed by the National Center for Supercomputing Applications ( NCSA ) at the University of Illinois at Urbana -Champaign , is gaining popularity as an easy to use point and click interface for searching portions of the Internet.


We investigate single-view algorithms as an alternative to multi-view algorithms for weakly supervised learning for natural language processing tasks without a natural feature split.

we apply co-training, self-training, and EM to one such task and find that both selftraining and FS-EM, a new variation of EM that incorporates feature selection, outperform cotraining and are comparatively less sensitive to parameter changes.


Initially, we use bagging (Breiman, 1996) to train a committee of classifiers using the labeled data.

each classifier is trained on a bootstrap sample created by randomly sampling instances with replacement from the labeled data until the size of the bootstrap sample is equal to that of the labeled data.


To ensure a fair comparison of the weakly supervised algorithms, the experiments are designed to determine the best parameter setting of each algorithm (in terms of its effectiveness to improve performance) for the data sets we investigate.

we keep the parameters common to all three weakly supervised algorithms (i.e.


Table 2 reproduces the 25 features of the coreference system and shows the views we employ.

the three view pairs are generated by the following methods.


Splitting of features according to the feature type.

one view comprises the lexicosyntactic features and the other the remaining ones.


At a high level, FS-EM uses a forward feature selection algorithm to impose a total ordering on the features based on the order in which the features are selected.

fS-EM performs the three steps below for each feature that has not been selected.


This algorithmic bias is particularly useful for weakly supervised learners (such as EM) that optimize an objective function other than classification accuracy and can potentially produce a classification that is substantially different from the actual one.

fS-EM attempts to ensure that the classification produced by the weakly supervised learner weakly agrees with the actual classification, where the weak disagreement rate between two classifications is defined as the difference between their positive class priors.


Before merging two nodes, we check to see whether the keyword lists associated with them share words with other nodes.

supposed we just merged nodes A and B, and they are expanded with the same syntactic rule into A 1 A 2 ...A n and B 1 B 2 ...B n respectively; before we merge each A i with B i , we check for each B i if its keyword list shares common words with any A j (j = i).


A natural choice is to pick the category c that has the largest posterior probability given the text.

using Bayes rule, this can be rewritten as where deducing Eq.


We now proceed to present our results on several text categorization problems on different languages.

we consider language identification, Greek authorship attribution, Greek genre classification, English topic detection, Chinese topic detection and Japanese topic detection.


In case of fragmentary input to the transfer component, grammaticaliy of the output is guaranteed for the separate fragments.

strings generated from a reduced fragmentary f -structure will be as grammatical as the string that was fed into the parsing component.


However, since sequential classifiers are trained to make the best local decision, unlike generative models they cannot trade off decisions at different positions against each other.

sequential classifiers are myopic about the impact of their current decision on later decisions (Bottou, 1991;Lafferty et al., 2001).


This is easily encoded by making the CRF labels pairs of consecutive chunk tags.

the label at position i is y i = c i−1 c i , where c i is the chunk tag of word i, one of O, B, or I.


The metric we use to evaluate the discourse segmenter records the accuracy of the discourse segmenter with respect to its ability to insert inside-sentence discourse boundaries.

if a sentence has 3 edus, which correspond to 2 inside-sentence discourse boundaries, we measure the ability of our algorithm to correctly identify these 2 boundaries.


In such models, the probability assigned to a tagged sequence of words x = t, w is the product of a sequence of local portions of the graphical model, one from each time slice.

for example, in the left-to-right CMM shown in figure 1(a), the replicated structure is a local model P(t 0 |t −1 , w 0 ).


An important feature of a comparative evaluation is that only relative scores are required.

the only requirement of the evaluation methodology for a comparative evaluation is that it reliably rank better methods ahead of worse methods.


The requirement that the question sets be disjoint ensures that the comparisons are made on independent samples of the space of questions.

we assume a universe of all possible closed-class questions, and an (unknown) probability distribution of the scores for each of the two runs.


The error rate for 500 questions when a difference of 0.05 in confidence-weighted scores is observed is approximately 8 %.

if we know nothing about two systems except their scores which differ by 0.05, and if we repeat the experiment on 100 different sets of 500 questions, then on average we can expect 8 out of those 100 sets to favor one system while the remaining 92 to favor the other.


While the task in earlier QA tracks had already been validated, changes to the 2002 task were significant enough to warrant further examination.

the 2002 task required systems to return exact answers, to return one response per question, and to rank questions by trials overlap, there may be correlations among the trials that could bias the estimates of the error rates as compared to what would be obtained with an equal number of samples drawn from a much larger initial set of questions.


Unfortunately, neither the initial task within the TREC QA track nor the TREC 2002 task produces a reusable QA test collection.

it is not currently possible to use the judgment set produced during TREC to accurately evaluate a QA run that uses the same document and question sets as the TREC runs but was not judged by the human assessors.


Using general loglinear regression (Poisson model) under the hypothesis that these two variables are independent of each other, our analysis showed that there is a systematic relationship (significance probability is 0.0504) between source of selected terms and answer accuracy.

in cases where subjects used more index terms identified by the human indexer, the answers were more accurate.


Consequently, approximately 61 percent of the generated sentences were judged good from the viewpoints of both Japanese correctness and translation effectiveness.

561 sentences on average were correctly generated, and the source equivalent sentence sets were expanded about 66 times.


In a hub automaton, the idea is more general than in previous work and applies to more complex morphologies, such as those for agglutinative or polysynthetic languages.

we are interested in unsupervised learning of Inuktitut morphology in which a single lexical unit can often include a verb, two pronouns, adverbs, and temporal information.


We introduce a constraint that uses the dependency tree of the English sentence to maintain phrasal cohesion in the French sentence.

if two phrases are disjoint in the English sentence, the alignment must not map them to overlapping intervals in the French sentence.


' Our model assigns a relevance measure of the structured query as a vector of relevance measures of the subqueries.

the relevance is defined by the number of portions matched with subqueries in a document.


Due to the variability of its stiffness, different places along the BM are sensitive to sounds with different spectral content.

the BM is stiff and thin at the base, but less rigid and more sensitive to low frequency signals at the apex.


we have experimented with the sequence of the previous n dialogue acts.


In this paper, we present a similar system with a much simpler set of model parameters.

we compute the probability of a block sequence b n 1 .


The non-vital concepts act as a "don't care" condition.

systems should be penalized for not retrieving vital concepts, and penalized for retrieving items that are not on the assessor's concept list at all, but should be neither penalized nor rewarded for retrieving a non-vital concept.


For practical reasons, the search space has to be reduced to a reasonably small set of hypotheses.

the starting probability is just one minus the maximum linking probability.


In the context of databases, several works have looked at the problem of record linkage -recognizing duplicate records in a database (Cohen and Richman, 2002;Hernandez and Stolfo, 1995;Bilenko and Mooney, 2003).

(Pasula et al., 2002) considers the problem of identity uncertainty in the context of citation matching and suggests a probabilistic model for that.


Model I: (the simplest model) The key relaxation here is in losing the notion of an "author" -rather than first choosing a representative for each document, mentions are generated independently and directly given an entity.

an entity e i is selected from E according to the prior probability P (e i ); then its actual mention m i is selected according to P (m i |e i ).


The first mention in each group is chosen as the representative.

when considering a mention m ∈ M d , P (m|r) is computed for each representative r that have already been created and a fixed threshold is then used to decide whether to create a new group for m or to add it to one of the existing groups with the largest P (m|r).


We incorporate these penalties into ψ b by introducing a binding agreement penalty, α(b), as follows: The remaining penalty functions model binding fertility.

we assign a penalty for each unassigned gesture and reference, ψ g (g) and ψ r (r) respectively, that reflect our desire for the algorithm to produce bindings.


The idea is that the derived domain-specific corpus would obviate the need for in-domain corpus collection.

we are interested in techniques that would result in corpora such that the performance of language models trained on these corpora would rival the performance of models trained on corpora collected specifically for a specific domain.


¡ Owing to the large set of features that are used for predicate identification and argument extraction, we estimate the probabilities using a classification model.

we use the Adaboost classifier (Freund and Schapire, 1996) wherein a highly accurate classifier is build by combining many "weak" or "simple" base classifiers 0 % , each of which may only be moderately accurate.


This section takes a closer look at the setup of the definition question evaluation at TREC 2003.

we examine three issues: the scoring metric, error inherent in the evaluation process, and variations in judgments.


On the other hand, a system with an ability of -3, would have the even odds decrease by a factor of 2.72 to .369, yielding P sq = .27.

increasing (decreasing) questions' difficulties or decreasing (increasing) systems' abilities by the same amounts affects the log-odds in the same fashion.


Systems' abilities mostly cover a lower range such that the questions' locations (Mean Q = 0 Logits) far exceed those of the systems (Mean S = -1.98 Logits).

most questions are very hard for these systems.


Rasch modeling formalizes the preceding account in a statistical fashion.

for each response to Question q by System s, Equation 1 allows the computation of a standardized residual z sq , which is the difference between an observed datum (i.e., 0 or 1) and the probability estimate P sq after division by its standard deviation.


Since the same systems are used in the simulation, and the questions have been calibrated to be on the same scale, the estimated system abilities S s as derived from the Easy and Hard question sets should be statistically identical.

these two estimates should show a high linear correlation and they should have very similar means and standard deviations (see e.g., Wright and Stone, 1979, p. 83-126).


However, Table 2 also shows that the standard deviations of the number correct estimates obtained for the Easy sets exceed those of the Hard sets as well (sometimes by over 100%).

when raw scores (or percentages) are used, the "spacing" of the systems is affected by the difficulty of the questions.


Based on our findings, we recommend that test equating be introduced in formal evaluations of HLT.

for the QA track of the TREC competition, we propose that NIST include a set of questions to be reused in the following year for calibration purposes.


We call such expressions in the same itemization hyponym candidates.

a set of the hyponym candidates extracted from a single itemization or list is called a hyponym candidate set (HCS).


Note that a process of generalization is performed automatically by treating all the hyponym candidates in an HCS simultaneously.

words strongly connected with only one hyponym candidate (for instance, "Lexus" for Toyota) have relatively low score values since we obtain statistical measures from all the local document sets for all the hyponym candidates in an HCS.


The final output of our procedure is the top k pairs in this ranking after some heuristic rules are applied to it in Step 4.

the procedure discards the remaining m − k pairs in the ranking because they tend to include erroneous hypernyms.


In addition, we assumed  Step 4 is the final output, this means that we also assumed that only the top 200 pairs of a hypernym and an HCS would be produced as final output with our procedure.

the remaining 1,800 (=2,000-200) pairs were discarded. )


(Recall that our algorithm uses the score only for sorting pairs of HCSs and their hypernym.

we do not re-rank the hypernym candidates for a single HCS.)


This may have caused some errors, but our patterns were more generous than those used in the previous studies, and did not miss the expressions matched to the patterns from the previous studies.

the accuracy obtained with our patterns was an upper bound on the performance obtained by the previous proposal.


Another difference was that the procedure was given correct pairs of a hypernym and a hyponym computed beforehand using our proposed method, and it only checked whether given pairs could be found by using the lexicosyntactic patterns from given texts.

this alternative method checked if the lexicosyntactic patterns could find the hypernym-hyponym pairs successfully obtained by our procedure.


We assumed that for a given HCS a hypernym was successfully acquired if one of the above methods could find the correct hypernym.

the performance of Alternative 4 would be achieved only when there were a technique to combine the output of the above methods in an optimal way.


Active learning (AL) methods, such as uncertainty sampling (Cohn et al., 1995) or query by committee (Seung et al., 1992), can dramatically reduce the cost of creating an annotated dataset.

they enable rapid creation of labeled datasets which can then be used for trainable speech and language technologies.


To understand whether product uncertainty is indeed choosing more wisely, it is important to consider the performance of an ensemble parse selection model when examples are chosen by a single-model AL method.

using a single-model AL method, but labeling examples using an ensemble model.


We also note that proper names appear in the DEP-BANK as single multi-word expressions without any internal structure.

there are no dependencies holding among the parts of people names (A. Boyd Simpson), company names (Goldman, Sachs & Co), and organization names (Federal Reserve).


Multiple initial states are not needed: we can use a single start state Q i , and instead of each initial state q with starting weight w add the rule (Q i , TRUE) → w (q, ()) (where TRUE(t) ≡ 1, ∀t).

we define the binary relation ⇒ X for xR tranducer X on T Σ∪∆∪Q ×(paths×R) * , pairs of partially transformed (working) trees and derivation histories: b is derived from a by application of a rule (q, pat) → w r to an unprocessed input subtree a ↓ i which is in state q, replacing it by output given by r, with its nonterminals replaced by the instruction to transform descendant input subtrees at relative path i ′ in state q ′ .


Our work, in contrast, treats content as a primary entity.

our induction algorithms are designed with the explicit goal of modeling document content, which is why they differ from the standard Baum-Welch (or EM) algorithm for learning Hidden Markov Models even though content models are instances of HMMs.


6 For evaluation purposes, we created corpora from five domains: earthquakes, clashes between armies and rebel groups, drug-related criminal offenses, financial reports, and summaries of aviation accidents.

7 the first four collections consist of AP articles from the North American News Corpus gathered via a TDT-style document clustering system.


Furthermore, to provide some assessment of the quality of the predicted orderings themselves, we follow Lapata (2003) in employing Kendall's § , which is a measure of how much an ordering differs from the OSOthe underlying assumption is that most reasonable sentence orderings should be fairly similar to it.

for a permutation¨of the sentences in an is the number of swaps of adjacent sentences necessary to re-arrange¨into the OSO.


Since we used two somewhat orthogonal tasks, ordering and summarization, to evaluate the quality of the contentmodel paradigm, it is interesting to ask whether the same parameterization of the model does well in both cases.

we looked at the results for different model topologies, induced by varying the number of contentmodel states.


This paper presents a system that detects various types of disfluencies and other structural information with cues obtained from lexical and prosodic information sources.

combinations of decision trees and language models are used to predict sentence ends and interruption points and, given these events, transformationbased learning is used to detect edit disfluencies and conversational fillers.


Filler performance in particular seems to be affected by recognition errors, which is not surprising, since misrecognized words would likely not be on the target lists of filled pauses and discourse markers.

nearly all missed and incorrectly inserted filled pauses are due to recognition errors.


To illustrate both points, consider the PAL A summary; its score in the n=3 row of .76 indicates that relatively much of its content is highly weighted.

with respect to the original pyramid with only three tiers, it contained a relatively high proportion of the top tier SCUs: 3/4 of the w=3 facts (75%).


With higher order pyramids, scores stabilize.

in our data, if summaries diverge at some point as in Figure 3, where the minimum score for the better summary is higher than the maximum score for the worse summary, the size of the divergence never decreases as pyramid order increases.


These loss functions can use information from word strings, word-to-word alignments and parse-trees of the source sentence and its translation.

we describe the design of a Bilingual Tree Loss Function that can explicitly use syntactic structure for measuring translation quality.


We note that the MAP decoder is not optimal in any of the cases.

the translation performance under the BLEU metric can be improved by using MBR relative to MAP decoding.


Given an MT evaluation metric of interest such as BLEU, PER or WER, we can use this metric as a loss function within the MBR framework to design decoders optimized for the evaluation criterion.

the MBR decoding under the BLEU loss function can yield further improvements on top of MAP decoding.


The text of the discourse element and the prompt (text of the essay question) must be related.

the thesis statement, main ideas, and conclusion statement should all contain text that is strongly related to the essay topic.


For a text to hold together, certain discourse elements must be related or the text will appear choppy and will be difficult to follow.

a high rank is assigned to each sentence in the background material, main ideas and conclusion that is related to the thesis, and supporting idea sentences that relate to the relevant main idea.


To make this determination, we use a support vector machine (SVM) classifier (Vapnik, 1995;Christianini and Shawe-Taylor, 2000).

we use an SVM with a radial basis function kernel, which exhibited good performance on a subset of about 30 essays from the pre-training data.


For each dimension, we also report the performance of a simple baseline measure, which assumes that all of our essay coherence criteria are satisfied.

our baseline assigns category 1 (high relevance) to every sentence, on every dimension.


As future work we propose to take the Wizard and Operator Test paradigm introduced herein and to change and adjust the parameters of the computer-human interaction -while performing subsequent measurements of the ensuing effects -until an acceptable degree of dialogue efficiency is reached.

finding out just how much extra-propositional signaling is needed to guarantee a felicitous dialogue.


However, it is apparent that one of these derivations seems much more "wrong" than the other.

in the second derivation, "pas" is replaced by the English word "he," which makes no sense.


The alignment A induced by D is created as follows: an element s of S is aligned with a leaf node t of T iff replaced(s, D) = created(t, D).

a source word is aligned with a target word if the target word is created during the same step in which the source word is replaced.


By subalignment, we mean that A ⊆ A (recall that alignments are simple mathematical relations).

a is a subalignment of a if a aligns two elements only if a also aligns them.


Nonetheless, Naive Bayes models seem to work well in practice when used as classifiers.

the choice that receives the highest probability (relative to the other choices) is often the correct one even though the actual probabilities the model generates may not be very good.


They are also sometimes called logistic regression models, maximum likelihood exponential models, log-linear models, and are even equivalent to a form of perceptrons/single layer neural networks.

perceptrons that use the standard sigmoid function, and optimize for log-loss are equivalent to maxent.


place a Gaussian prior with 0 mean and σ 2 i variance on the model parameters (the λ i s), and then find a model that maximizes the posterior probability of the data and the model.

maxent models without priors use the parameters Λ that maximize where x j , y j are training data instances.


In particular, maxent models without priors use the parameters Λ that maximize where x j , y j are training data instances.

with a Gaussian prior we find In this case, a trained model does not satisfy the constraints expected[i] = observed [i], but, as Chen and Rosenfeld show, instead satisfies constraints instead of a model that matches the observed count, we get a model matching the observed count minus λi σ 2 i : in language modeling terms, this is "discounting."


We do not believe that all models are generated by the same process, and therefore we do not believe that a single prior will work best for all problem types.

as we will describe in our experimental results section, when looking at one particular set of parameters, we noticed that it was not at all Gaussian, but much more similar to a 0 mean Laplacian, , which is non-zero only for non-negative λ i .


With an exponential prior, we maximize As we will describe, it is significantly simpler to perform this maximization than the Gaussian maximization.

furthermore, as we will describe, models satisfying Equation 2 will have the property that, for each λ i , either a) λ i = 0 and we essentially just discount the observed counts by the constant α i (which is the reciprocal of the standard deviation), subject to the constraint that λ i is non-negative.